rx_btrees

Usage

revoscalepy.rx_btrees(formula, data, output_file=None, write_model_vars=False,
    overwrite=False, pweights=None, fweights=None, cost=None, min_split=None,
    min_bucket=None, max_depth=1, cp=0, max_compete=0, max_surrogate=0,
    use_surrogate=2, surrogate_style=0, n_tree=10, m_try=None, replace=False,
    strata=None, sample_rate=None, importance=False, seed=None,
    loss_function='bernoulli', learning_rate=0.1, max_num_bins=None,
    max_unordered_levels=32, remove_missings=False, compute_obs_node_id=None,
    use_sparse_cube=False, find_splits_in_parallel=True, row_selection=None,
    transforms=None, transform_objects=None, transform_function=None,
    transform_variables=None, transform_packages=None, blocks_per_read=1,
    report_progress=2, verbose=0, compute_context=None, xdf_compression_level=1,
    **kwargs)

Description

Fit stochastic gradient boosted decision trees on an .xdf file or data frame for small or large data using parallel external memory algorithm.

Arguments

formula

Statistical model using symbolic formulas.

data

Either a data source object, a character string specifying a ‘.xdf’ file, or a data frame object. If a Spark compute context is being used, this argument may also be an RxHiveData, RxOrcData, RxParquetData or RxSparkDataFrame object or a Spark data frame object from pyspark.sql.DataFrame.

output_file

Either an RxXdfData data source object or a character string specifying the ‘.xdf’ file for storing the resulting node indices. If None, then no node indices are stored to disk. If the input data is a data frame, the node indices are returned automatically.

write_model_vars

Bool value. If True, and the output file is different from the input file, variables in the model will be written to the output file in addition to the node numbers. If variables from the input data set are transformed in the model, the transformed variables will also be written out.

overwrite

Bool value. If True, an existing output_file with an existing column named outColName will be overwritten.

pweights

Character string specifying the variable of numeric values to use as probability weights for the observations.

fweights

Character string specifying the variable of integer values to use as frequency weights for the observations.

method

Character string specifying the splitting method. Currently, only “class” or “anova” are supported. The default is “class” if the response is a factor, otherwise “anova”.

parms

Optional list with components specifying additional parameters for the “class” splitting method, as follows:

prior: A vector of prior probabilities. The priors must be positive and sum to 1. The default priors are proportional to the data counts.

loss: A loss matrix, which must have zeros on the diagonal and positive off-diagonal elements. By default, the off-diagonal elements are set to 1.

split: The splitting index, either gini (the default) or information.

If parms is specified, any of the components can be specified or omitted. The defaults will be used for missing components.

cost

A vector of non-negative costs, containing one element for each variable in the model. Defaults to one for all variables. When deciding which split: to choose, the improvement on splitting on a variable is divided by its cost.

min_split

The minimum number of observations that must exist in a node before a split is attempted. By default, this is sqrt(num of obs). For non-XDF data sources, as (num of obs) is unknown in advance, it is wisest to specify this argument directly.

min_bucket

The minimum number of observations in a terminal node (or leaf). By default, this is min_split/3.

cp

Numeric scalar specifying the complexity parameter. Any split that does not decrease overall lack-of-fit by at least cp is not attempted.

max_compete

The maximum number of competitor splits retained in the output. These are useful model diagnostics, as they allow you to compare splits in the output with the alternatives.

max_surrogate

The maximum number of surrogate splits retained in the output. See the Details for a description of how surrogate splits are used in the model fitting. Setting this to 0 can greatly improve the performance of the algorithm; in some cases almost half the computation time is spent in computing surrogate splits.

use_surrogate

An integer specifying how surrogates are to be used in the splitting process: 0: Display-only; observations with a missing value for the primary split variable are not sent further down the tree.

1: Use surrogates, in order, to split observations missing the primary split variable. If all surrogates are missing, the observation is not split.

2: Use surrogates, in order, to split observations missing the primary split variable. If all surrogates are missing or max_surrogate=0, send the observation in the majority direction.

The 0 value corresponds to the behavior of the tree function, and 2 (the default) corresponds to the recommendations of Breiman et al.

surrogate_style

An integer controlling selection of a best surrogate. The default, 0, instructs the program to use the total number of correct classifications for a potential surrogate, while 1 instructs the program to use the percentage of correct classification over the non-missing values of the surrogate. Thus, 0 penalizes potential surrogates with a large number of missing values.

n_tree

A positive integer specifying the number of trees to grow.

m_try

A positive integer specifying the number of variables to sample as split candidates at each tree node. The default values are sqrt(num of vars) for classification and (num of vars)/3 for regression.

replace

A bool value specifying if the sampling of observations should be done with or without replacement.

cutoff

(Classification only) A vector of length equal to the number of classes specifying the dividing factors for the class votes. The default is 1/(num of classes).

strata

A character string specifying the (factor) variable to use for stratified sampling.

sample_rate

A scalar or a vector of positive values specifying the percentage(s) of observations to sample for each tree: for unstratified sampling: A scalar of positive value specifying the percentage of observations to sample for each tree. The default is 1.0 for sampling with replacement (i.e., replace=True) and 0.632 for sampling without replacement (i.e., replace=False).

for stratified sampling: A vector of positive values of length equal to the number of strata specifying the percentages of observations to sample from the strata for each tree.

importance

A bool value specifying if the importance of predictors should be assessed.

seed

An integer that will be used to initialize the random number generator. The default is random. For reproducibility, you can specify the random seed either using set.seed or by setting this seed argument as part of your call.

compute_oob_error

An integer specifying whether and how to compute the prediction error for out-of-bag samples: <0: never. This option may reduce the computation time. =0: only once for the entire forest.

0: once for each addition of a tree. This is the default.

loss_function

Character string specifying the name of the loss function to use. The following options are currently supported: “gaussian”: Regression: for numeric responses. “bernoulli”: Regression: for 0-1 responses. “multinomial”: Classification: for categorical responses with two or more levels.

learning_rate

Numeric scalar specifying the learning rate of the boosting procedure.

max_num_bins

The maximum number of bins to use to cut numeric data. The default is min(1001, max(101, sqrt(num of obs))). For non-XDF data sources, as (num of obs) is unknown in advance, it is wisest to specify this argument directly. If set to 0, unit binning will be used instead of cutting. See the ‘Details’ section for more information.

max_unordered_levels

The maximum number of levels allowed for an unordered factor predictor for multiclass (>2) classification.

remove_missings

Bool value. If True, rows with missing values are removed and will not be included in the output data.

use_sparse_cube

Bool value. If True, sparse cube is used.

find_splits_in_parallel

bool value. If True, optimal splits for each node are determined using parallelization methods; this will typically speed up computation as the number of nodes on the same level is increased.

row_selection

None. Not currently supported, reserved for future use.

transforms

None. Not currently supported, reserved for future use.

transform_objects

None. Not currently supported, reserved for future use.

transform_function

Variable transformation function. The variables used in the transformation function must be specified in transform_variables if they are not variables used in the model.

transform_variables

List of strings of input data set variables needed for the transformation function.

transform_packages

None. Not currently supported, reserved for future use.

blocks_per_read

Number of blocks to read for each chunk of data read from the data source.

report_progress

integer value with options: 0: No progress is reported. 1: The number of processed rows is printed and updated. 2: Rows processed and timings are reported. 3: Rows processed and all timings are reported.

verbose

Integer value. If 0, no additional output is printed. If 1, additional summary information is printed.

compute_context

A RxComputeContext object for prediction.

kwargs

Additional parameters

Returns

A RxDForestResults object of dtree model.

See also

rx_predict, rx_predict_rx_dforest.

Example

import os
from revoscalepy import rx_btrees, rx_import, RxOptions, RxXdfData
sample_data_path = RxOptions.get_option("sampleDataDir")
ds = RxXdfData(os.path.join(sample_data_path, "kyphosis.xdf"))
kyphosis = rx_import(input_data = ds)

# classification
form = "Kyphosis ~ Age + Number + Start"
seed = 0
n_trees = 50
interaction_depth = 4
n_min_obs_in_node = 1
shrinkage = 0.1
bag_fraction = 0.5
distribution = "bernoulli"

btree = rx_btrees(form, kyphosis, loss_function=distribution, n_tree=n_trees, learning_rate=shrinkage,
                sample_rate=bag_fraction, max_depth=interaction_depth, min_bucket=n_min_obs_in_node, seed=seed,
                replace=False, max_num_bins=200)

# regression
ds = RxXdfData(os.path.join(sample_data_path, "airquality.xdf"))
df = rx_import(input_data = ds)
df = df[df['Ozone'] != -2147483648]

form = "Ozone ~ Solar.R + Wind + Temp + Month + Day"
ntrees = 50
interaction_depth = 3
min_obs_in_node = 1
shrinkage = 0.1
bag_fraction = 0.5
distribution = "gaussian"

btree = rx_btrees(form, df, loss_function=distribution, n_tree=ntrees, learning_rate=shrinkage,
                  sample_rate=bag_fraction, max_depth=interaction_depth, min_bucket=min_obs_in_node, seed=0,
                  replace=False, max_num_bins=200, verbose=2)